
"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     '''altclip_text_model'''


						def __init__(			self  :   Tuple	,A_  :   List[str]=25_0002	,A_  :   Tuple=1024	,A_  :   Dict=24	,A_  :   Union[str, Any]=16	,A_  :   str=4096	,A_  :   str="gelu"	,A_  :   List[str]=0.1	,A_  :   Optional[int]=0.1	,A_  :   Tuple=514	,A_  :   int=1	,A_  :   Union[str, Any]=0.02	,A_  :   Dict=0.02	,A_  :   Dict=1e-05	,A_  :   int=1	,A_  :   List[Any]=0	,A_  :   Optional[Any]=2	,A_  :   int="absolute"	,A_  :   Optional[int]=True	,A_  :   Dict=768	,**A_  :   Tuple	,)  -> str:
							super().__init__(pad_token_id=A_	,bos_token_id=A_	,eos_token_id=A_	,**A_   )

							A     = vocab_size
							A     = hidden_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = hidden_act
							A     = intermediate_size
							A     = hidden_dropout_prob
							A     = attention_probs_dropout_prob
							A     = max_position_embeddings
							A     = type_vocab_size
							A     = initializer_range
							A     = initializer_factor
							A     = layer_norm_eps
							A     = position_embedding_type
							A     = use_cache
							A     = project_dim







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Union[str, Any]   =     '''altclip_vision_model'''


						def __init__(			self  :   int	,A_  :   Any=768	,A_  :   List[str]=3072	,A_  :   str=512	,A_  :   Optional[Any]=12	,A_  :   str=12	,A_  :   str=3	,A_  :   Dict=224	,A_  :   Optional[int]=32	,A_  :   Optional[Any]="quick_gelu"	,A_  :   int=1e-5	,A_  :   Union[str, Any]=0.0	,A_  :   Union[str, Any]=0.02	,A_  :   int=1.0	,**A_  :   List[Any]	,)  -> Optional[int]:
							super().__init__(**A_   )

							A     = hidden_size
							A     = intermediate_size
							A     = projection_dim
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = num_channels
							A     = patch_size
							A     = image_size
							A     = initializer_range
							A     = initializer_factor
							A     = attention_dropout
							A     = layer_norm_eps
							A     = hidden_act




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Optional[Any]	,A_  :   Union[str, os.PathLike]	,**A_  :   str   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							# get the vision config dict if we are loading from AltCLIPConfig
							if config_dict.get('model_type'   ) == "altclip":
								A     = config_dict['vision_config']

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Any   =     '''altclip'''
						_lowerCamelCase:     List[str]   =     True


						def __init__(			self  :   Optional[Any]	,A_  :   str=None	,A_  :   Tuple=None	,A_  :   str=768	,A_  :   Any=2.65_92	,**A_  :   int   )  -> Optional[int]:
							# If `_config_dict` exist, we use them for the backward compatibility.
							# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
							# of confusion!).
							A     = kwargs.pop('text_config_dict'	,A_   )
							A     = kwargs.pop('vision_config_dict'	,A_   )

							super().__init__(**A_   )

							# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
							# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
							# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
							if text_config_dict is not None:
								if text_config is None:
									A     = {}

								# This is the complete result when using `text_config_dict`.
								A     = AltCLIPTextConfig(**A_   ).to_dict()

								# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
								for key, value in _text_config_dict.items():
									if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
										# If specified in `text_config_dict`
										if key in text_config_dict:
											A     = (
											    F'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
											    F'The value `text_config_dict["{key}"]` will be used instead.'
											)
										# If inferred from default argument values (just to be super careful)
										else:
											A     = (
											    F'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
											    F'value `text_config["{key}"]` will be overriden.'
											)
										logger.warning(A_   )

            # Update all values in `text_config` with the ones in `_text_config_dict`.
								text_config.update(_text_config_dict   )

							if vision_config_dict is not None:
								if vision_config is None:
									A     = {}

								# This is the complete result when using `vision_config_dict`.
								A     = AltCLIPVisionConfig(**A_   ).to_dict()
								# convert keys to string instead of integer
								if "id2label" in _vision_config_dict:
									A     = {
									    str(A_   ): value for key, value in _vision_config_dict['id2label'].items()
									}

								# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
								for key, value in _vision_config_dict.items():
									if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
										# If specified in `vision_config_dict`
										if key in vision_config_dict:
											A     = (
											    F'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
											    F'values. The value `vision_config_dict["{key}"]` will be used instead.'
											)
										# If inferred from default argument values (just to be super careful)
										else:
											A     = (
											    F'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
											    F'The value `vision_config["{key}"]` will be overriden.'
											)
										logger.warning(A_   )

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
								vision_config.update(_vision_config_dict   )

							if text_config is None:
								A     = {}
								logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.'   )

							if vision_config is None:
								A     = {}
								logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.'   )

							A     = AltCLIPTextConfig(**A_   )
							A     = AltCLIPVisionConfig(**A_   )

							A     = projection_dim
							A     = logit_scale_init_value
							A     = 1.0


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Tuple	,A_  :   AltCLIPTextConfig	,A_  :   AltCLIPVisionConfig	,**A_  :   List[Any]   )  -> Dict:
							return cls(text_config=text_config.to_dict()	,vision_config=vision_config.to_dict()	,**A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[int]:
							A     = copy.deepcopy(self.__dict__   )
							A     = self.text_config.to_dict()
							A     = self.vision_config.to_dict()
							A     = self.__class__.model_type
							return output
"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square(__lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												# BASE CASE
												if row >= rows or col >= cols:
																		return 0

												lowerCamelCase__   =update_area_of_max_square(__lowerCAmelCase  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						update_area_of_max_square(0  ,       0							)
						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square_using_dp_array(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												if row >= rows or col >= cols:
																		return 0
												if dp_array[row][col] != -1:
																		return dp_array[row][col]

												lowerCamelCase__   =update_area_of_max_square_using_dp_array(__lowerCAmelCase  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		lowerCamelCase__   =sub_problem_sol
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						lowerCamelCase__   =[[-1] * cols for _ in range(__lowerCAmelCase							)]
						update_area_of_max_square_using_dp_array(0  ,       0  ,       __lowerCAmelCase							)

						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[[0] * (cols + 1) for _ in range(rows + 1							)]
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =dp_array[row][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(dp_array[row][col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0

						return largest_square_area





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =current_row[col + 1]
																		lowerCamelCase__   =next_row[col + 1]
																		lowerCamelCase__   =next_row[col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(current_row[col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0
												lowerCamelCase__   =current_row

						return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


